Computational design and molecular characterization of a multi-epitope peptide vaccine targeting pancreatic cancer.

Computational design and molecular characterization of a multi-epitope peptide vaccine targeting pancreatic cancer - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice J Genet Eng Biotechnol . 2026 Apr 10;24(2):100670. doi: 10.1016/j.jgeb.2026.100670 Search in PMC Search in PubMed View in NLM Catalog Add to search Computational design and molecular characterization of a multi-epitope peptide vaccine targeting pancreatic cancer Seyed Mostafa Rahimi Seyed Mostafa Rahimi a Student Research Committee, Babol University of Medical Sciences, Babol, Iran b Cellular and Molecular Biology Research Center, Health Institute, Babol University of Medical Sciences, Babol, Iran Find articles by Seyed Mostafa Rahimi a, b , Hamid Reza Nouri Hamid Reza Nouri c Immunoregulation Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran d USERN Office, Babol University of Medical Sciences, Babol, Iran Find articles by Hamid Reza Nouri c, d, ⁎ Author information Article notes Copyright and License information a Student Research Committee, Babol University of Medical Sciences, Babol, Iran b Cellular and Molecular Biology Research Center, Health Institute, Babol University of Medical Sciences, Babol, Iran c Immunoregulation Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran d USERN Office, Babol University of Medical Sciences, Babol, Iran ⁎ Corresponding author at: Cellular and Molecular Biology Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran. [email protected] Received 2025 Apr 12; Revised 2025 Dec 30; Accepted 2026 Feb 26; Collection date 2026 Jun. © 2026 Published by Elsevier Inc. on behalf of Academy of Scientific Research and Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13091744  PMID: 42309580 Graphical abstract Open in a new tab Keywords: Computational biology, Dynamics simulation, Immunoinformatic, Multi-epitope vaccine, Pancreatic cancer Highlights • Key peptide-based antigens were used in the optimal design of the proposed vaccine. • In-silico experiments validated the stability and functionality of the vaccine. • The vaccine demonstrates the potential to induce efficient immune responses against pancreatic cancer . Abstract Pancreatic cancer (PC) is a major global health burden, characterized by high mortality rates. Immunotherapy-based strategies have shown promise in combating malignant diseases such as PC. Among these, cancer vaccines are gaining considerable interest as one of the most effective immunotherapeutic approaches. Therefore, in this study, we aimed to develop an effective peptide-based vaccine against PC via immunoinformatic techniques. To construct the vaccine, antigenic proteins associated with PC, including ITGA2, LMAC2, BMPR2, ZFP91, and S100A16, were selected. The predicted epitopes were assembled using proper linkers, and to improve immunogenicity, PADRE and β-defensin 2 sequences were incorporated into the final construct. Further analysis validated the chimeric structure as potentially antigenic, non-allergenic, non-toxic, and soluble. The integrity of its secondary and tertiary structures was also confirmed. Additionally, molecular docking and dynamics simulation of TLR4 demonstrate that the vaccine–receptor complex is structurally stable. The immune simulation results further indicated the vaccines’ ability to induce strong cellular and humoral responses. Codon optimization and in silico cloning into an Escherichia coli expression system were also successfully performed. Based on our findings, the proposed vaccine shows promise for targeting PC, warranting further experimental validation in animal models. 1. Introduction Pancreatic cancer (PC) is one of the most lethal malignancies worldwide. Currently, it ranks seventh among cancer-related causes of death globally, and it is expected to rise to the third position shortly. Despite recent medical advances, the 5-year survival rate for PC has not significantly improved. 1 , 2 , 3 , 4 In recent years, extensive research has paved the way for more effective treatment strategies against aggressive cancers like PC, with immunotherapy emerging as a particularly promising strategy. 5 Immunotherapy aims to boost the immune system's ability to specifically recognize and eliminate cancer cells while sparing healthy tissues, thereby reducing complications compared to conventional treatments. Notably, due to the generation of robust and long-lasting immune responses, its therapeutic effects may be sustained, potentially even long-lasting, possibly lifelong. 6 , 7 , 8 Among different immunotherapy approaches, cancer vaccines have shown promising potential in both experimental and clinical settings. These vaccines target tumor-specific antigens (TSAs) or tumor-associated antigens (TAAs) that are expressed on the surface of malignant cells and play critical roles in tumor progression. Cancer vaccines utilize these antigens, introduced as nucleic acids or protein constructs, to train the immune system to recognize and combat tumor cells effectively. 9 , 10 , 11 , 12 A pivotal step in the vaccine development is the identification of suitable TSAs or TAAs. This requires selecting the ideal protein targets for constructing a multi-epitope vaccine framework. Recent studies have identified several promising antigenic proteins for pancreatic cancer that are capable of inducing a specific immune response. Among several potential candidates, we selected S100A16, ITGA2, LAMC2, BMPR2, and ZFP91 based on literature evidence highlighting their overexpression in pancreatic tumors, involvement in cancer progression, and immunological relevance. While other antigens may also be suitable, this set provides a focused framework for computational vaccine design. S100A16, a member of the S100 family of Ca2 + -binding proteins, is significantly overexpressed in pancreatic tumors compared to healthy tissue. Immunoinformatics studies have suggested a negative correlation between the expression of S100A16 and immune infiltration, where lower expression of this protein correlates with a better prognosis in PC. It also influences immune cell populations, including B and T lymphocyte populations. 13 Additionally, the S100A16 molecule promotes metastasis, invasion, and cell proliferation of pancreatic ductal adenocarcinoma (PDAC) via ERK1/2 and AKT signaling. Its knockdown has been associated with tumor suppression. 14 Furthermore, recent studies have demonstrated that integrin alpha-2 precursor (ITGA2) and laminin subunit gamma-2 (LAMC2) are also overexpressed in PC at the protein level. Their increased expression is involved in enhancing cancer cell metastasis, invasion, and chemoresistance, contributing to disease progression. 15 , 16 , 17 , 18 , 19 Therefore, ITGA2 and LAMC2 serve as attractive candidates for inclusion in a multi-epitope vaccine targeting malignant pancreatic tumors. Bone morphogenetic protein receptor 2 (BMPR2), part of the transforming growth factor-β (TGF-β) superfamily, is another promising antigenic target. Elevated BMPR2 expression has been detected in several cancers, including PC, where it contributes to tumor growth. Its inhibition has led to the arrest of the cell cycle and suppressed pancreatic tumors via the GRB2/PI3K/AKT signaling pathway. 20 , 21 Finally, Zinc finger protein 91 (ZFP91), an E3 ubiquitin-protein ligase, is overexpressed in pancreatic tumor tissues and is recognized as a poor prognostic factor. Through activation of β‐catenin signaling, ZFP91 promotes invasion, migration, and proliferation of malignant cells, as well as chemoresistance and epithelial-to-mesenchymal transition (EMT). Silencing of ZFP91 has been shown to decrease tumor cell migration, invasion, and proliferation. 22 Considering its role in tumor progression, ZFP91 offers a strong candidate for therapeutic intervention. Multi-epitope vaccine technology allows for the stimulation of broad immune responses by incorporating several highly immunogenic epitopes, while minimizing adverse allergic reactions associated with whole-protein vaccines. 23 , 24 The rapid advancements in bioinformatic approaches have further facilitated vaccine development by providing cost-effective and time-efficient methods that have demonstrated safety. 25 , 26 Taken together, PC remains an aggressive malignancy with limited effectiveness of conventional therapies. However, emerging strategies such as cancer vaccines hold significant promise. In this study, we designed a multi-epitope peptide vaccine using epitopes derived from ITGA2, S100A16, LAMC2, BMPR2, and ZFP91, utilizing high-throughput bioinformatics algorithms. The proposed vaccine was predicted to elicit robust immune responses, offering a promising approach for future therapeutic development against PC. 2. Methods 2.1. Study design The entire workflow was conducted using in silico approaches. A summary of the methodological pipeline is illustrated in Fig. 1 . Fig. 1. Open in a new tab Schematic representation of the methodology used for the design of a multi-epitope vaccine targeting pancreatic cancer. The workflow includes antigen selection, epitope prediction, and construction of a multi-epitope vaccine candidate. The designed construct underwent physicochemical characterization, as well as secondary and tertiary structure prediction. Molecular docking with Toll-like receptor 4 (TLR4) was performed, followed by molecular dynamics (MD) simulation, immune response simulation, and in silico cloning to evaluate the stability and immunogenic potential of the vaccine candidate. The figure was created using BioRender (Scientific Image and Illustration Software | BioRender.com ). 2.2. Sequence retrieval and homology analysis Following a comprehensive survey, five critical antigenic proteins with therapeutic potential in pancreatic cancer were identified. The amino acid sequences of the selected proteins including ZFP91 (Accession No: NP_444251.1 , NP_001183980.1 , Q96JP5-1, Q96JP5-2), S100A16 (Accession No: NP_525127.1 , NP_001303936.1 , NP_001303937.1 , Q96FQ6-1), BMPR2 (Accession No: NP_001195.2 , XP_011509989.1 , Q13873-1, Q13873-2), LAMC2 (Accession No: NP_005553.2 , NP_061486.2 , XP_016856762.1 , Q13753-1, Q13753-2) and ITGA2 ( NP_002194.2 , P17301-1) were obtained from the National Center for Biotechnology Information (NCBI) ( https://www.ncbi.nlm.nih.gov/protein/ ) and UniProt databases at ( https://www.uniprot.org ), in FASTA format. To identify conserved fragments across the different isoforms of each immunogenic protein, sequence alignment was performed using BioEdit software. 2.3. MHC I immunogenic epitope prediction The proper binding of epitopes to MHC molecules and the subsequent formation of peptide-MHC complexes, followed by presentation to T cells, are critical early steps in triggering effective immune responses. 27 Class-I restricted epitopes recognized by CD8 + T cells, such as cytotoxic T lymphocytes (CTLs), play a central role in the acquired immunity in the direct attack against tumors. 28 To identify appropriate MHC I-bound epitopes, TepiTool ( https://tools.iedb.org/tepitool/ ) was utilized. TepiTool is an algorithm-based tool that allows users to customize parameters according to their specific needs. For computational prediction, we selected the IEDB-recommended method for MHC class I binding based on prior predictive performance and method availability. The IEDB strategy utilizes a hierarchy of methods, including Consensus, ANN, SMM, NetMHCpan, and CombLib, in that order. A default threshold of ≤ 1.0 percentile rank was used for the prediction of epitopes. 29 In addition, we used the MHC class I immunogenicity server ( https://tools.iedb.org/immunogenicity/ ) to further refine the selection of epitopes with high immunogenicity potential. This model evaluates epitopes based on the enrichment of specific amino acids and their position on the MHC-I presented peptide. 30 The MHC I epitopes with the highest predicted binding affinity and immunogenicity from both tools were chosen for developing the main construct of the vaccine. 2.4. MHC II immunogenic epitope prediction Epitopes presented by antigen-presenting cells (APCs) via MHC II molecules can activate helper T lymphocytes (HTLs). 31 Activated T-helper cells, as critical elements of the acquired immune system, significantly contribute to establishing a well-organized humoral and cellular immune response against pathogenic factors, including cancer-related cells. 32 To select appropriate MHC II-restricted epitopes, the TepiTool server ( https://tools.iedb.org/tepitool/ ) was employed. Similar to the MHC I approach, the IEDB recommended method was used for the computational prediction of the epitopes. The predictions were performed using the default cut-off value; however, unlike MHC I, the cutoff value for MHC II is defined as a percentile rank ≤ 10.0. 29 Additionally, the IEDB CD4 T cell immunogenicity prediction server ( http://tools.iedb .org/CD4episcore/) was utilized to evaluate the immunogenicity of MHC II epitopes. This tool generates an immunogenicity score based on neural network algorithms. 33 , 34 The final selection of suitable MHC II epitopes was based on the combined results from both tools. 2.5. Prediction of IFN-γ and IL-4-inducing epitopes and assessing toxicity status To assess the potential of predicted epitopes in the induction of interferon-gamma (IFN-γ), a key cytokine involved in immune regulation, the IFN-γ epitope server ( http://crdd.osdd.net/raghava/ifnepitope/scan.php ) was used. This server utilizes a prediction method based on support vector machine (SVM) algorithms. IL-4 is another important cytokine that significantly impacts the direction of the immune response. To identify IL-4-inducing epitopes, the IL4pred server ( https://webs.iiitd.edu.in/raghava/il4pred/design.php ) was utilized, which applies a hybrid prediction method combining SVM and motif-based approaches. 2.6. Design of vaccine candidate construct and optimization of chimeric gene To achieve a subunit vaccine structure capable of eliciting immunological responses, predicted epitopes from selected antigens were fused using proper amino acid linker sequences such as AAY, EAAAK, HEYGAEALERAG, GGGS, and GPGPG. The N-terminus of the multi-epitope vaccine sequence begins with a methionine. Following that, the HIV TAT peptide, a well-known cell-penetrating peptide (CPP), was added. 35 Subsequently, β-defensin 2, an efficient adjuvant, was incorporated to boost immune responses. 36 HTL and CTL epitopes from the selected antigenic proteins were then successively attached to the construct. Finally, a His-tag sequence was inserted at the C-terminal region of the construct to facilitate further purification assays. 37 Back-translation of the final protein sequence into DNA, along with codon usage for E. coli expression, was performed via the JCAT online tool ( https://www.jcat.de ). 38 2.7. mRNA secondary structure prediction The secondary structure of the chimeric gene’s mRNA was predicted using the Mfold online server ( https://www.unafold.org/mfold/applications/rna-folding-form.php ). This server predicts the minimum ΔG and most probable base pairing of the proposed structure via thermodynamic methods. 39 2.8. Evaluation of antigenicity and allergenicity To evaluate the antigenicity of the candidate vaccine, the VaxiJen v2.0 ( http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html ) 40 and ANTIGENPro ( http://scratch.Proteomics . ics.Uci.edu/ ) 41 servers were utilized. The VaxiJen, which can discriminate between antigens and non-antigens with an accuracy of 70% to 89%, assesses antigenicity based on the physicochemical properties of the protein’s amino acid composition. Allergenicity prediction was done by multiple prediction methods, including AlgPred ( http://www.imtech.res.in/raghava/algpred/ ), a freely accessible server. The SVM algorithm on this server, with a threshold of − 0.4, achieves approximately 85% accuracy. 42 AllergenFP v.1.0 ( https://ddg-pharmfac.net/AllergenFP/ ), a descriptor-based fingerprint method, was also used for calculations of allergenicity. 43 Additionally, to ensure the safety of the vaccine candidate, the cytotoxicity of the predicted epitopes was evaluated using the ToxinPred server ( https://webs.iiitd.edu.in/raghava/toxinpred3/ motif.php). 2.9. Prediction of the physicochemical parameters and solubility properties Using the pKa values of amino acids along with the amino acid composition of the protein, several physicochemical parameters of the vaccine were evaluated using the ProtParam tool ( https://web.expasy.org/protparam/ ). 44 This online server predicts the grand average of hydropathicity (GRAVY), 45 molecular weight, extinction coefficient, 46 estimated half-life, 47 counts of positively and negatively charged residues, instability index, 48 theoretical isoelectric point, and aliphatic index. 49 Additionally, SOLpro server ( https://scratch.proteomics.ics.uci.edu ), which employs a two-step SVM method, was used to assess the solubility upon overexpression in Escherichia coli . 50 2.10. Secondary structure prediction The GOR IV ( https://npsa-prabi.ibcp.fr/cgibin/npsa_automat.pl page=/NPSA/npsa_gor4.html) 51 and PSIPRED 4.0 ( http://bioinf.cs.ucl.ac.uk/psipred/ ) 52 tools were utilized to predict the secondary structure of the vaccine candidate. These tools were used to identify the coils (C), helices (H), and strands (S) in the protein sequence. The GOR IV algorithms perform predictions based on Bayesian statistics and information theory. 51 PSIPRED uses a Position-Specific Iterated BLAST (PSI-BLAST) search followed by analysis through feed-forward neural network-based algorithms to generate accurate secondary structure predictions. 52 2.11. Tertiary structure prediction RaptorX ( https://raptorx.uchicago.edu/StructurePrediction/predict/ ) was utilized for fully automated prediction of the final vaccine construct tertiary structure. This tool excels in aligning challenging targets according to the similarity between the target sequence and available templates to determine the most probable tertiary structure. After importing the target protein sequence in FASTA format, RaptorX applies several advanced approaches, including single-template threading, alignment quality prediction, and multiple-template threading, to generate the top predicted structural models for that primary target. 53 , 54 2.12. Validating the tertiary structure To enhance the quality of the proposed 3D protein construct acquired from the earlier bioinformatics analysis, the GalaxyRefine tool was used. After importing the 3D protein structure model, the server processes data and provides five refined models along with information on GDT-HA, RMSD, and MolProbity score. 55 Following refinement, the SAVES v6.0 server ( https://saves.mbi.ucla.edu/ ) and the ProSA server ( https://prosa.services.came.sbg.ac.at /prosa.php) were used to validate the refined model. 56 , 57 Additionally, the Zlab tool ( https://zlab.umassmed.edu/bu/rama/ ) was utilized to generate a Ramachandran plot, aiding in the recognition of potential structural flaws in the protein model. 2.13. Prediction of intrinsic protein disorder DisEMBL 1.5 58 ( https://dis.embl.de/ ) and IUPred ( https://iupred.enzim.hu/pred.php ) 59 were employed to identify potential unstructured regions and intrinsic protein disorders within the protein construct of the vaccine candidate. 2.14. Linear and conformational B-cell epitopes prediction To identify both linear and conformational B-cell epitopes, the ElliPro online server ( https://tools.iedb.org/ellipro/ ) was utilized. This web tool makes epitope prediction and visualization via a hybrid approach combining Thornton’s approach, cluster-based algorithms, MODELLER software, and the Jmol program. 60 2.15. Disulfide bond engineering For the disulfide engineering of the proposed construct, the web-based program Disulfide by Design v2.0 (DbD2) ( https://cptweb.cpt.wayne.edu/DbD2/index.php ) was used. DbD2 identifies residue pairs that are compatible with disulfide bond formation by analyzing structural parameters and choosing pairs with the potential to form stable disulfide bonds. 61 2.16. Docking of the vaccine candidate with TLR4, MHC-I, and MHC-II To characterize the molecular interaction between the proposed vaccine and the key immune receptors in their stable conformation, molecular docking was carried out. Docking and molecular dynamics simulation of the multi-epitope construct to TLR4, MHC-I, and MHC-II were performed to assess structural compatibility and complex stability. The HDOCK online server ( https://hdock.phys.hust.edu.cn/ ) was utilized. The HDOCK server conducts protein–protein docking through a hybrid approach that combines ab initio free docking with template-based modelling. 62 Following the docking analysis, the complex with the lowest binding energy was selected. 2.17. Molecular dynamics simulation Molecular dynamics simulation (MDS), which evaluates the physical movements of atoms and molecules over a fixed period, enables the analysis of a system’s dynamic evolution. To assess the interactive dynamics between the vaccine candidate and TLR4, MDS was performed using the Groningen Machine for Chemical Simulations (GROMACS) software. 63 The GROMACS package v2024, which includes a comprehensive set of computational tools, was used to simulate the Newtonian equations of motion for systems with a large number of particles, primarily through a command-line interface. In addition to GROMACS, VMD, and PyMOL software were used for visualization purposes. The system was enclosed in a triclinic simulation box, and the Amber99SB force field was applied to define simulation parameters. TIP3P water molecules were used to solvate the complex, and Na + and Cl − ions were added to neutralize the system’s net charge. Energy minimization was performed using the steepest descent algorithm for 100 ps, with the termination criterion set at Fmax < 1000 kJ/mol/nm. This was followed by equilibration steps: NVT and NPT, both carried out for 100 ps. The Nose–Hoover thermostat and Parrinello–Rahman barostat were used to maintain the temperature at 300 K and pressure at 0 bar. Following these steps, MD simulation of the vaccine-TLR4 complex was conducted. To evaluate the behavior of the complex during the simulation, root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (Rg) were calculated. Furthermore, to complement the time-limited MDS results from GROMACS, the iMODS web server ( http://imods.chaconlab.org/ ) was utilized to assess extended molecular dynamics analysis based on normal mode analysis (NMA). We used this method for evaluating MDS status between the proposed vaccine and the key immune receptors, including TLR4, MHC-I, and MHC-II. This approach predicts the collective motion of protein molecules with notable precision. Parameters such as deformability, B-factor, eigenvalues, variance, covariance maps, and the elastic network were analyzed, and corresponding graphical outputs were generated. 64 , 65 , 66 2.18. In silico cloning The pET21b(+) vector was selected to facilitate the cloning of the proposed vaccine construct. The pET expression system is widely used for high-level expression of recombinant proteins in Escherichia coli due to its strong regulatory elements. However, limitations of the pET system, including misfolding of proteins or absence of eukaryotic post-translational modifications, impact the protein function. 67 , 68 , 69 Initially, via utilizing a web-based tool, the codon-optimized sequence of the recombinant construct was reverse tranlated. 38 Then BamHI and NotI restriction sites were added to the N- and C-termini of the sequence, respectively, to secure the compatibility with the vector’s translation direction. Following this step, an optimized sequence was inserted into the pET21b(+) vector between BamHI and NotI restriction sites using the SnapGene restriction cloning module. 2.19. Immune simulation To evaluate the potential of the proposed construct to induce immune responses, the C-ImmSim server ( https://150.146.2.1/C-IMMSIM/index.php ) was utilized. This server performs immune interaction and immunogenicity predictions using a position-specific scoring matrix (PSSM) combined with machine learning techniques and immune epitope prediction algorithms. C-ImmSim simulates immune responses in three distinct mammalian anatomical regions: the thymus, bone marrow, and a tertiary lymphoid organ. 70 2.20. Population coverage prediction To estimate the population coverage of the candidate vaccine across the global population, the IEDB tool ( http://tools.iedb.org/population/ ) was employed. 71 The population coverage calculations for MHC molecules were carried out separately on the IEDB server. 2.21. Computational settings and default parameters All analyses were performed using the default parameters of the respective tools, unless otherwise noted. This applies to servers and tools used for: MHC I and II epitope prediction (TepiTool, MHC I/II immunogenicity), IFN-γ and IL-4 prediction, antigenicity and allergenicity assessment (VaxiJen, ANTIGENPro, AlgPred, AllergenFP), toxicity (ToxinPred), physicochemical and solubility analysis (ProtParam, SOLpro), secondary/tertiary structure prediction (GOR IV, PSIPRED, RaptorX), intrinsic disorder prediction (DisEMBL, IUPred), B-cell epitope prediction (ElliPro), disulfide engineering (DbD2), docking (HDOCK), molecular dynamics (iMODS), immune simulation (C-ImmSim), and population coverage analysis (IEDB). 3. Results 3.1. Initial evaluations of obtained sequences FASTA sequences of five proteins, including ZFP91, S100A16, BMPR2, LAMC2, and ITGA2, were acquired from UniProt and NCBI databases. Best immunogenic and non-toxic, MHC-I, and MHC-II epitopes were determined using proper bioinformatics tools ( Table 1 and Table 2 , respectively). In addition, the multi-stage engineering of the constructed vaccine has been illustrated in Fig. 1 . Table 1. The list of predicted immunogenic MHC-I epitopes. Antigen Epitope Position IFN-γ Toxicity Allele Percentile rank Immunogenicity score ZFP91 EIKVEVEVEV 542–551 + Non-toxic HLA-A*68:02 0.11 0.29683 ZFP91 TDQRDYICEY 557–566 + Non-toxic HLA-B*15:01 0.17 0.17076 ZFP91 SHNLAVHRM 572–580 _ Non-toxic HLA-C*07:04 0.11 0.12124 HLA-C*07:01 0.14 HLA-C*06:02 0.29 HLA-C*14:02 0.49 HLA-C*04:01 0.74 S100A16 RISFDEYWTL 204–213 + Non-toxic HLA-A*32:01 0.15 0.38934 HLA-A*02:06 0.51 HLA-A*02:01 0.57 S100A16 ISFDEYWTL 219–227 + Non-toxic HLA-B*58:01 0.2 0.36396 HLA-B*53:01 0.68 S100A16 SFDEYWTLI 233–241 + Non-toxic HLA-C*04:01 0.02 0.33914 HLA-C*07:04 0.31 HLA-C*05:01 0.45 HLA-C*08:02 0.58 HLA-C*14:02 0.65 HLA-C*07:02 0.92 HLA-C*01:02 0.96 BMPR2 RVPWLPWTI 91–99 + Non-toxic HLA-A*32:01 0.1 0.40767 HLA-A*23:01 0.3 HLA-A*24:02 0.32 HLA-A*02:06 0.55 HLA-A*32:01 0.74 HLA-A*02:01 0.91 BMPR2 RVPWLPWTIL 105–114 + Non-toxic HLA-B*07:02 0.19 0.51672 BMPR2 QRPWRVPWL 120–128 + Non-toxic HLA-C*06:02 0.06 0.42861 HLA-C*07:01 0.15 HLA-C*07:04 0.27 LAMC2 SVMPETEEVV 317–326 _ Non-toxic HLA-A*02:06 0.17 0.27001 HLA-A*68:02 0.27 HLA-A*02:01 0.6 HLA-A*02:03 0.71 LAMC2 HPSAHDVIL 332–340 _ Non-toxic HLA-B*07:02 0.06 0.15065 HLA-B*35:01 0.05 HLA-B*53:01 0.13 HLA-B*08:01 0.41 HLA-B*51:01 0.41 LAMC2 VMPETEEVV 346–354 _ Non-toxic HLA-C*01:02 0.09 0.33782 ITGA2 SMATVIIHI 430–438 + Non-toxic HLA-A*02:03 0.04 0.34846 HLA-A*02:01 0.06 HLA-A*02:06 0.11 HLA-A*32:01 0.16 HLA-A*68:02 0.52 ITGA2 QQVTFTINF 444–452 _ Non-toxic HLA-B*15:01 0.07 0.31154 HLA-B*44:03 0.77 HLA-B*44:02 0.84 ITGA2 SCPEHIIYI 458–466 + Non-toxic HLA-C*01:02 0.04 0.36409 HLA-C*07:04 0.28 HLA-C*17:01 0.3 HLA-C*06:02 0.34 HLA-C*15:02 0.38 Open in a new tab Table 2. The list of predicted immunogenic MHC- II epitopes. Antigen Epitope Position IL4 Toxicity Allele Percentile rank Immunogenicity score ZFP91 LQHHIKYQHLLKKKY 593–607 + Non-toxic HLA-DRB4*01:01 0.2 76.2198 ZFP91 QHHIKYQHLLKKKYV 611–625 + Non-toxic HLA-DPA1*03:01/DPB1*04:02 3.1 76.7357 ZFP91 EIKVEVEVEVKEEEN 629–643 + Non-toxic HLA-DQA1*03:01/DQB1*03:02 0.58 95.6973 S100A16 VSKYSLVKNKISKSS 254–268 + Non-toxic HLA-DRB1*11:01 1.1 65.4594 S100A16 LVENFYKYVSKYSLV 272–286 + Non-toxic HLA-DPA1*01:03/DPB1*02:01 4.9 75.973 S100A16 YWTLIGGITGPIAKL 290–304 − Non-toxic HLA-DQA1*05:01/DQB1*03:01 4.3 92.0197 BMPR2 ALCFGYRMLTGDRKQ 141–155 + Non-toxic HLA-DRB5*01:01 0.89 61.3767 BMPR2 VAVKVFSFANRQNFI 159–173 + Non-toxic HLA-DPA1*02:01/DPB1*05:01 1.7 76.738 BMPR2 EVGTIRYMAPEVLEG 177–191 + Non-toxic HLA-DQA1*05:01/DQB1*02:01 1.6 82.5705 LAMC2 FEYRRLLRNLTALRI 367–381 − Non-toxic HLA-DRB1*04:05 0.35 67.2274 LAMC2 PQLSYFEYRRLLRNL 385–399 + Non-toxic HLA-DPA1*01:03/DPB1*02:01 1.4 81.6421 LAMC2 HRLITQMQLSLAESE 403–417 + Non-toxic HLA-DQA1*01:02/DQB1*06:02 1.7 97.1691 ITGA2 VGLIQYANNPRVVFN 479–493 + Non-toxic HLA-DRB1*13:02 0.37 66.0732 ITGA2 VAILWKLGFFKRKYE 497–511 + Non-toxic HLA-DPA1*02:01/DPB1*05:01 0.93 55.3923 ITGA2 ESTHFVAGAPRANYT 515–529 + Non-toxic HLA-DQA1*05:01/DQB1*03:01 0.94 87.2422 Open in a new tab 3.2. MHC epitope selection Twelve MHC-I and twelve MHC-II epitopes, predicted using the IEDB server along with their percentile ranks, are demonstrated in Table 1 and Table 2 , respectively. The immunogenicity scores of these epitopes, obtained from the IEDB immunogenicity server, are recorded in these tables. The IEDB tools were configured based on the most frequent HLA alleles in the human population. Additionally, the results obtained from the IL4Pred server and IFN-γ epitope prediction server for each chosen epitope are presented in Table 1 and Table 2 . 3.3. Chimeric multi-epitope vaccine construct Twenty-four validated MHC class I and II epitopes, linked using appropriate peptide linkers, form the primary structure of the vaccine construct. These linkers play a critical role in enhancing the flexibility and structural stability of the construct. In addition, the vaccine includes a TAT sequence to improve cellular delivery, β-defensin 2 as an adjuvant, PADRE sequences to induce robust T-cell responses, and a His-tag for purification purposes. A schematic representation of the vaccine structure is shown in Fig. S1 . 3.4. Adaptation of the codon and mRNA construct of the proposed vaccine To generate a modified DNA sequence optimized for replication and overexpression in E. coli strain K12, the JCAT online server was employed. The optimized sequence was 1983 nucleotides long, with a GC content of 52.84% and a codon adaptation index (CAI) of 0.967, indicating high compatibility with the host. To calculate the free energy of the proposed mRNA structure, the Mfold server was utilized. The predicted secondary RNA structure had a minimum free energy of –719.9 kcal/mol at 37 °C , and the engineered structure showed no hairpins or pseudoknots at the 5′ end ( Fig. 2 A-C). Fig. 2. Open in a new tab Adaptation of codon and modified secondary mRNA structure. (A) Relative adaptiveness of the predicted codons. (B) Evaluation of entropy in mRNA construct. The minimal free energy of the predicted mRNA construct was equal to −719.9 kcal/mol at 37 °C. (C) Representation of predicted mRNA structure. There were no hairpins or pseudoknots at the 5′ terminus of the mRNA. 3.5. Evaluation of antigenicity, allergenicity, physicochemical features, and solubility of the proposed construct Evaluation of the construct allergenicity using AlgPred server and AllergenFP, both of which utilize various algorithms for prediction, indicated that the vaccine has a non-allergenic nature. The VaxiJen and ANTIGENPro servers confirmed the designed sequence as a probable antigen. The molecular weight (Mw) and theoretical isoelectric point (pI) of the vaccine were calculated to be 73.7 kDa and 8.64, respectively. The vaccine structure comprises 661 amino acids. The grand average of hydropathy (GRAVY) was calculated to be −0.413 for the vaccine candidate. With an aliphatic index of 77.14 and an instability index of 31.07, the vaccine was predicted to be stable. In addition, the estimated half-life of the vaccine construct was > 30 h in mammalian reticulocytes ( in vitro ), >20 h i n yeast ( in vivo ), and > 10 h in E. coli ( in vivo ). The solubility analysis showed that the construct would remain soluble after overexpression with a probability of 0.988671 . Furthermore, comparisons of the physicochemical properties of the proposed vaccine with other recently published in silico-designed vaccines based on similar methodologies revealed comparable results 72 , 73 , 74 , 75 ( Table S1 ), further supporting the stability and feasibility of the proposed construct. 3.6. Determination of secondary and tertiary structure of vaccine candidate The online tool analyzed the secondary structure of the proposed construct and showed that it is comprised of 49.31% alpha-helix, 14.52% beta-strand, and 36.17% coil structural components ( Fig. 3A ). To determine the tertiary structure of the designed construct, the RaptorX server was utilized. Following a fully automated process, a predicted 3D model of the construct was generated ( Fig. 3B ). Fig. 3. Open in a new tab (A) Graphical illustration of the vaccine’s secondary structure. The 3-D predicted construct of the vaccine before (B) and after (C) the refinement process. Following refinement, the Z-score of the construct was reduced from −9.33 to −9.67, which is representative of improved quality. 3.7. Refinement process and in silico validation of the 3D construct Tertiary structure refinement on the initial 3D model of the vaccine construct was carried out via the GalaxyRefine computational server. Based on ProSA server results, the Z-score of the designed vaccine after the refinement process fell within the acceptable range of scores for experimentally determined structures (obtained via X-ray crystallography and NMR). The Z-score improved from −9.33 to −9.67 following the refinement process ( Fig. 3C ). Additionally, analysis of generated Ramachandran plots illustrated that, in the refined model, the number of residues in questionable regions decreased, while residues in highly preferred regions increased. The refined construct showed 540 residues (98.4%) in highly preferred regions, 9 residues (1.6%) in preferred regions, and 0 residues (0.0%) in the questionable areas ( Fig. 4 ). Fig. 4. Open in a new tab Ramachandran plot of initial and refined vaccine construct. Highly preferred, preferred, and questionable observations are demonstrated in green crosses, brown triangles, and red circles, respectively. The values of observation improved following the refinement process. From 92.896% to 98.361% in highly preferred observations, from 5.829% to 1.639% in preferred observations, and from 1.275% to 0.000% in questionable observations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3.8. Intrinsic protein disorder prediction The DisEMBL server results are expressed in three different categories, including loops/coils, hot loops, and remark-465. Predicted disordered regions based on the loops/coils definition were found at positions 9–27, 36–64, 85–128, 145–153, 179–192, 203–235, 295–302, 314–350, 437–462, 480–490, 517–531, 549–576, and 653–661. According to the hot loops definition, disordered regions were identified at positions 1–29, 43–63, 84–94, and 651–661. Predicted disorder was observed at positions 1–20, 55–74, 321–332, and 635–661. The illustrative plot of intrinsic protein disorder prediction is provided in Fig. S2 . 3.9. Prediction of B-cell epitopes Continuous and discontinuous B-cell epitopes located within the vaccine structure were identified via the ElliPro server. A summary of the predicted epitopes is provided in Table 3 and Table 4 . Moreover, the structural localization of B-cell epitopes is demonstrated in Fig. 5 . Table 3. The list of predicted linear B–cell epitopes. No. Start End Peptide Number of residues Score 1 584 661 GAEALERAGLQHHIKYQHLLKKKYAAYQHHIKYQHLLKKKYVAAYEIKVEVEVEVKEEENHEYGAEALERAGHHHHHH 78 0.876 2 1 78 MEAAAKGRKKRRQRRRPPQGGGSGIINTLQKYYCRVRGGRCAVLSCLPKEEQIGKCSTRGRKCCRRKKEAAAKAKFVA 78 0.776 3 298 357 TGPIAKLHEYGAEALERAGSVMPETEEVVGPGPGHPSAHDVILGPGPGVMPETEEVVHEY 60 0.75 4 94 131 WLPWTIGPGPGRVPWLPWTILGPGPGQRPWRVPWLHEY 38 0.674 5 471 489 AEALERAGVGLIQYANNPR 19 0.655 6 384 397 YPQLSYFEYRRLLR 14 0.634 7 548 561 EVEVGPGPGTDQRD 14 0.576 8 151 159 GDRKQAAYV 9 0.535 9 252 255 AGVS 4 0.523 10 181 184 IRYM 4 0.503 Open in a new tab Table 4. The list of predicted discontinuous B–cell epitopes. No. Residues Number of residues Score 1 A:K639, A:E640, A:E642, A:N643 4 0.904 2 A:V547, A:V549, A:E550, A:V551, A:G552, A:P553, A:G554, A:P555, A:G556, A:T557, A:D558, A:Q559, A:R560, A:H581, A:Y583, A:G584, A:A585, A:E586, A:A587, A:L588, A:E589, A:R590, A:A591, A:G592, A:L593, A:Q594, A:H595, A:H596, A:I597, A:K598, A:Y599, A:Q600, A:H601, A:L602, A:L603, A:K604, A:K605, A:K606, A:Y607, A:A608, A:A609, A:Y610, A:Q611, A:H612, A:H613, A:I614, A:K615, A:Q617, A:H618, A:L619, A:L620, A:K621, A:Y624, A:V625, A:A626, A:A627, A:Y628, A:E629, A:I630, A:K631, A:V632, A:E633, A:V634, A:E635, A:V636, A:E637, A:V638, A:E641, A:H644, A:E645, A:Y646, A:G647, A:A648, A:E649, A:A650, A:L651, A:E652, A:R653, A:A654, A:G655, A:H656, A:H657, A:H658, A:H659, A:H660 85 0.816 3 A:T298, A:G299, A:P300, A:I301, A:A302, A:K303, A:L304, A:H305, A:E306, A:Y307, A:G308, A:A309, A:E310, A:A311, A:L312, A:E313, A:R314, A:A315, A:G316, A:S317, A:V318, A:M319, A:P320, A:E321, A:T322, A:E323, A:E324, A:V325, A:V326, A:G327, A:P328, A:G329, A:P330, A:G331, A:H332, A:P333, A:S334, A:A335, A:H336, A:D337, A:V338, A:I339, A:L340, A:G341, A:P342, A:G343, A:P344, A:G345, A:V346, A:M347, A:P348, A:E349, A:T350, A:E351, A:E352, A:V353, A:V354, A:H355, A:E356, A:Y357, A:I381, A:A382, A:Y384, A:P385, A:Q386, A:L387, A:S388, A:Y389, A:F390, A:E391, A:Y392, A:R393, A:R394, A:L396, A:R397 75 0.726 4 A:M1, A:E2, A:A3, A:A4, A:A5, A:K6, A:G7, A:R8, A:K9, A:K10, A:R11, A:R16, A:P17, A:P18, A:Q19, A:G20, A:G21, A:G22, A:S23, A:G24, A:I25, A:I26, A:N27, A:T28, A:L29, A:Q30, A:K31, A:Y32, A:Y33, A:C34, A:R35, A:V36, A:R37, A:G38, A:G39, A:R40, A:C41, A:A42, A:V43, A:L44, A:S45, A:C46, A:L47, A:P48, A:K49, A:E50, A:E51, A:Q52, A:I53, A:G54, A:K55, A:C56, A:S57, A:T58, A:R59, A:G60, A:R61, A:K62, A:C63, A:C64, A:R65, A:R66, A:K67, A:K68, A:E69, A:A70, A:A71, A:A72, A:K73, A:A74, A:K75, A:F76, A:V77, A:A78, A:A85, A:A86, A:G87, A:G88, A:G89, A:S90, A:W94, A:L95, A:P96, A:W97, A:T98, A:I99, A:G100, A:P101, A:G102, A:P103, A:G104, A:R105, A:V106, A:P107, A:W108, A:L109, A:P110, A:W111, A:T112, A:I113, A:L114, A:G115, A:P116, A:G117, A:P118, A:G119, A:Q120, A:R121, A:P122, A:W123, A:R124, A:V125, A:P126, A:W127, A:L128, A:E130, A:Y131, A:E134, A:A135, A:R138, A:L149, A:G151, A:D152, A:R153, A:K154, A:Q155, A:A156, A:A157, A:Y158 129 0.71 5 A:I435, A:A471, A:E472, A:A473, A:L474, A:E475, A:R476, A:A477, A:G478, A:V479, A:G480, A:L481, A:I482, A:Q483, A:Y484, A:A485, A:N486, A:N487, A:P488, A:R489 20 0.642 6 A:E423, A:E426, A:R427 3 0.545 7 A:A252, A:G253, A:V254, A:S255 4 0.523 8 A:E137, A:V178, A:G179 3 0.501 Open in a new tab Fig. 5. Open in a new tab Representation of predicted discontinuous and linear B-cell epitopes in the 3-D model of the vaccine. Yellow surfaces on the vaccine structures are the epitope indicators. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3.10. Disulfide bond engineering of the ultimate construct The disulfide engineering process can notably improve the regional stability of the vaccine construct. Potential residue pairs suitable for mutation into cysteine residues to form disulfide bonds were identified using the DbD2 server. Screening criteria included a chi3 value between −87 and +97, and an energy value below the 2.2 threshold. The results demonstrated two residue pairs, TYR32-ALA70 and MET347-THR350, as candidates for disulfide bond formation within the vaccine construct. The mutation sites on the proposed vaccine construct are shown in Fig. 6A . Fig. 6. Open in a new tab (A) Positions of potential residues involved in the formation of disulfide bonds in the vaccine construct. 32TYR-70ALA and 347MET-350THR are detected as eligible residue pairs that could mutate and form disulfide bonds. (B) Molecular docking of the proposed vaccine with TLR4. 3.11. Study of molecular docking with TLR4, MHC-I, and MHC-II To simulate docking of the final construct with TLR4, MHC-I, and MHC-II complex structures, the HDOCK server was utilized. The 3D structure of these molecules were retrieved from the Protein Data Bank with the PDB ID of 4G8A, 4U6Y, and 1AQD for TLR4, MHC-I, and MHC-II, respectively. The resulting docked complexes are demonstrated in Fig. 6B , and Fig. S3 . 3.12. Molecular dynamics simulation The vaccine-TLR4 complex selected in the last step was subjected to molecular dynamics analyses using GROMACS software. Initially, energy minimization of the system was performed (Fmax < 1000 KJ/mol/nm) ( Fig. 7A ), a necessary process in MD simulation. The temperature progression graph illustrated that the system quickly stabilized at 300 K, a desirable value for the simulation, and remained constant during the rest of the simulation process ( Fig. 7B ). Similar to temperature, the system pressure rapidly reached the target value of 0 bar and experienced only slight fluctuations during simulation ( Fig. 7C ). These results validated the equilibration of the vaccine and TLR4 complex. The trajectories obtained from the MD simulation were utilized to assess RoG, RMSD, and RMSF evaluations. As shown in Fig. 7D , the RoG graph remained relatively constant, supporting compactness and stability of the target complex. The RMSD plot showed that after an initial adjustment period, the values stabilized within the range of 0.5 to 1.2 nm, indicating significant structural stability ( Fig. 7E ). Additionally, the computed RMSF value was used to evaluate the flexibility level in our target complex. Peak patterns in the RMSF plot represent regions of higher flexibility ( Fig. 7F ). Furthermore, visualization of the complex throughout the MD simulation confirmed its equilibrium and stability. Fig. 7. Open in a new tab Molecular dynamics simulation of vaccine-TLR4. The docking results and binding interaction between the vaccine and TLR4 were validated using GROMACS. The outcomes of various simulation steps are shown. (A) energy profile during minimization (kJ/mol vs. time in ps; Fmax < 1000 kJ/mol·nm), (B) temperature stabilization during the NVT step (K vs. time in ps; stabilized at 300  K), (C) pressure profile (bar vs. time in ps; stabilized around 0  bar), (D) radius of gyration (RoG) (nm vs. simulation time in ps; remained relatively constant), (E) root mean square deviation (RMSD) (nm vs. simulation time in ps; stabilized between 0.5–1.2  nm), and (F) root mean square fluctuation (RMSF) (nm vs. Atom number; peak fluctuations observed at flexible regions. These parameters were used to assess whether the protein structure remained stable or underwent conformational expansion. The iMOD web server was utilized to complete NMA of the vaccine–immune receptor complexes, including TLR4, MHC-I, and MHC-II. The deformability plot ( Fig. 8A , Fig. S4 A and Fig. S5 A) highlights regions of predicted structural distortion. The B-factor graph, which quantifies atomic fluctuations based on NMA, is presented in Fig. 8B , Fig. S4 B and Fig. S5 B. The predicted numerical value of eigenvalue, an indicator of the structure’s motion, was calculated as 8.351068e-07, 2.705131e-06, and 2.018124e-06 for TLR4, MHC-I, and MHC-II, respectively ( Fig. 8C , Fig. S4 C, and Fig. S5 C), and the structure’s variance, which is inversely proportional to the eigenvalue, is shown in Fig. 8D , Fig. S4 D, and Fig. S5 D. The covariance matrix ( Fig. 8E , Fig. S4 E, and Fig. S5 E) shows correlated, uncorrelated, and anti-correlated motions between residue pairs in red, white, and blue, respectively. Elastic network analysis was also conducted to demonstrate the rigid areas of the complex. The darker grays in the elastic network graph indicated stiffer springs ( Fig. 8F , Fig. S4 F and Fig. S5 F). Fig. 8. Open in a new tab Normal mode analysis results of the vaccine construct and the TLR4 complex (using iMODS online server). (A) Main-chain deformability plot highlighting regions of predicted structural distortion, (B) B-factor graph showing atomic fluctuations based on NMA, (C) eigenvalue of 8.351068e-07 indicating the structure’s motion, (D) variance plot, which is inversely proportional to the eigenvalue, (E) covariance matrix showing correlated (red), uncorrelated (white), and anti-correlated (blue) motions between residue pairs, and (F) elastic network analysis indicating rigid areas with darker gray regions representing stiffer springs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3.13. In silico replication of the vaccine A robust protein expression ability in the host ( E. coli ) was discovered following reverse translation and codon optimization of the vaccine sequence. The GC content of the optimized vaccine was 52.84%. The cloning process was performed using SnapGene software. To clone the proposed vaccine sequence, the pET21b(+) vector was utilized. The restriction sites for NotI and BamHI were added at the N-terminal and C-terminal, respectively, to facilitate insertion into the vector. Notably, to enable the ligation process, 5′ overhangs of the sequence were filled with complementary nucleotides. Then, the optimized sequence with restriction sites was inserted into the vector via restriction cloning. Overall, the vaccine candidate construct comprised 7424 base pairs ( Fig. 9 ). Fig. 9. Open in a new tab In silico cloning of the proposed vaccine into the pET21b (+) expression vector. The optimized sequence of the designed vaccine is presented in red, and the backbone of the vector is shown in black. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3.14. Immune simulation According to the computational immune assay results, potent humoral and cellular immune responses were detected following the initial injection and booster dosages ( Fig. 10 ). Significant increases in IgM and IgG levels were detected after booster administrations. While antigen (Ag) was almost completely neutralized shortly after each injection, the antibody (Ab) titers reached elevated levels that were sustained for an extended time ( Fig. 10A ). B cells and both helper and cytotoxic T cell populations increased significantly following booster injections, especially memory and active subtypes ( Fig. 10 B-H). Increased levels of B cells and T-helper cells were consistent with increased immunoglobulin levels observed during the simulation process. The presence of memory cells demonstrates the effectiveness of the proposed vaccine in establishing long-term immunity. Notably, both the proportion and absolute number of Th1 cells increased, suggesting a potential shift in the Th1/Th2 balance ( Fig. 10F ). Additionally, the dendritic cell (DC) population was boosted ( Fig. 10I ). NK cells and macrophages, which play critical roles in innate immune responses against tumors, also increased following both primary and booster injections ( Fig. 10 J, K). Furthermore, a favorable increase in IL-2 and IFN-γ levels was observed ( Fig. 10L ). Overall, the immune simulation data indicated that vaccine elicited strong immune responses, which may be associated with a favorable prognosis in cancer immunotherapy. Fig. 10. Open in a new tab In silico immune simulation of the proposed vaccine. (A) Immunoglobulin levels following immunization. (B-C) Induced responses of B cells, (D-F) CD4 + T-helper lymphocytes (HTLs), and (G-H) CD8 + T-cytotoxic lymphocytes (CTLs) cellular populations after vaccine injection. Innate immunity cellular population status includes (I) dendritic cells (DC cells), (J) macrophages, and (K) Natural killer cells (NK cells). (L) Cytokines and interleukin amounts after vaccination. 3.15. Population coverage The IEDB server was utilized to evaluate the population coverage of our proposed vaccine across the global human population. The analyzed results revealed that the vaccine had over 99% worldwide population coverage. This suggests that the MHC-I and MHC-II allele-targeted vaccine could be effective in the majority of the global population. However, due to the limited list of available HLA alleles in the IEDB database, the actual coverage rate may vary across different regions. The results of the population coverage analysis for MHC molecules are presented in Fig. 11 and Table 5 , Table 6 . Fig. 11. Open in a new tab Report of population coverage analysis in MHC-I and II, worldwide. Table 5. Population coverage evaluation of the designed vaccine for MHC-I epitopes, worldwide. Population/area Class I Coverage a Average_hit b pc90 c World 99.02% 6.35 2.79 Average 99.02 6.35 2.79 Standard deviation 0 0 0 Open in a new tab a. projected population coverage. b. average number of epitope hits / HLA combinations recognized by the population. c. minimum number of epitope hits / HLA combinations recognized by 90% of the population. Table 6. Population coverage evaluation of designed vaccine for MHC- II epitopes, worldwide. Population/area Class II Coverage a Average_hit b pc90 c World 99.66% 6.07 3.58 Average 99.66 6.07 3.58 Standard deviation 0 0 0 Open in a new tab a. projected population coverage. b. average number of epitope hits / HLA combinations recognized by the population. c. minimum number of epitope hits / HLA combinations recognized by 90% of the population. 4. Discussion According to the analyzed data, pancreatic cancer (PC) is expected to become the second leading cause of cancer-related deaths in the United States by 2030. 76 As a result, discovering effective strategies is urgently needed. Immunotherapy-based vaccines represent a promising and emerging approach in cancer treatment. The multi-epitope vaccines have shown promise in preclinical investigations. 77 , 78 , 79 , 80 In the present study, a multi-epitope peptide vaccine against PC was designed by harnessing advanced immunoinformatics-based algorithms. Tumor biomarkers, typically genes or proteins, are those whose expression is altered in the presence of a tumor or altered as part of the host’s response to tumor development. Their detection is crucial for tumor diagnosis, prognosis, and therapeutic targeting. When utilized in immunotherapy, these biomarkers are referred to as tumor antigens. Expression profiles of various tumor-associated antigens across different cancer types are summarized in Table 7 . To target pancreatic cancer, we selected several tumor antigens upregulated in pancreatic tumors for inclusion in a multi-epitope vaccine. This approach, widely adopted by other studies ( Table 8 ), has shown promising results. Importantly, multi-epitope cancer vaccines have demonstrated promising results not only in silico but also in clinical trials ( Table 9 ). These findings support the potential of rationally designed multi-epitope vaccines, such as the one proposed in this study, for effective cancer immunotherapy. Table 7. Differential expression patterns of upregulated and downregulated antigens in different tumors. Cancer origin Upregulated antigens Downregulated antigens Pancreas* HE4, G RP, FGFR, ZEB1, CEA, TOMM22, CA72-4, CA242, CA50, EN1, ITGA2†, BMPR2†, ZFP91† RPL15, Cathepsin W Liver PIVKA-II, HK2, HGF, GLUT1, AFP, Ferritin, PDGFR-α Annexin A6, HCRP1 Colorectum CEA, LDH, CA72-4, CA242, KRAS, ULK-1 GRP, PTEN, BAX Kidney GLUT1, PD-L1, Lamin B1, TPS, LDH PTEN, PDGF Bladder MMPs, ZEB2, CTLA-4, CapG E-cadherin Esophagus MLKL, Slug, TNF-α, SCCA, TPS, ULK-1 MMP1, BAX, LC3B Stomach ZEB2, CEA Open in a new tab † These indicated antigens with upregulated expression alongside LAMC2 and S100A16 were used for designing a multi-epitope vaccine targeting pancreatic cancer. Table 8. Overview of previous studies that utilized immunoinformatics approaches for cancer vaccine development, targeting tumor-associated antigens. Cancer type Infectious (Yes/No) Publication doi Adult T-cell leukemia Y 10.2174/1570164617999200717232832 Breast N 10.1080/07391102.2021.1883111 10.1016/j.jprot.2018.01.004 10.30476/ijms.2019.82301.1029 10.1016/j.compbiolchem.2020.107231 10.7324/JAPS.2021.110604 10.7314/APJCP.2012.13.7.3053 Cervical cancer Y 10.1016/j.meegid.2021.105084 10.1038/s41598-021-91997-4 10.1016/j.meegid.2020.104266 10.1371/journal.pone.0138686 10.1016/j.biologicals.2014.11.001 10.1007/s12033-021-00374-z Cholangiocarcinoma N 10.1016/j.jhep.2016.06.027 Colorectal cancer N Y 10.2147/DDDT.S231958 10.18632/oncotarget.26680 10.1186/s40425-017-0270-1 10.1186/s12859-023-05197-0 10.1038/s41598-019-55613-w Gastric cancer N Y 10.1016/j.yexcr.2020.111953 10.1016/j.vaccine.2010.12.130 10.1007/s10989-020-10157-w 10.1016/j.meegid.2017.02.007 Gallbladder cancer N 10.3390/vaccines10111850 Glioblastoma N 10.21203/rs.3.rs-198797/v2 10.1016/j.intimp.2020.107265 Hepatocellular carcinoma N 10.1097/CJI.0000000000000274 10.3892/or.2013.2531 Kaposi sarcoma Y 10.1038/s41598-019-39299-8 Lung cancer N 10.1016/j.imu.2023.101169 10.1080/14712598.2021.1981285 10.31557/APJCP.2021.22.5.1495 10.31557/APJCP.2020.21.8.2297 10.1080/2162402X.2016.1238539 MALT lymphoma Y 10.1016/j.micpath.2021.104970 Melanoma N 10.1080/07391102.2020.1846625 10.1080/2162402X.2017.1319028 10.1016/j.jconrel.2016.02.035 10.1007/s00262-011-1110-7 Mesothelioma N 10.3389/fonc.2019.00720 Myeloma N 10.1111/bjh.14686 Nasopharyngeal carcinoma Y 10.1038/cmi.2015.29 Prostate cancer N Y 10.1007/s12013-020-00912-7 10.1016/j.meegid.2020.104282 Open in a new tab Table 9. Multi-epitope vaccines in clinical trials. Vaccine name Target cancer type Target antigens/ epitopes Trial phase Status/notes Publication doi DPX-Survivac Ovarian Survivin epitopes Phase II Tested in combination with cyclophosphamide Well-tolerated, with clinical benefit 10.1158/1078-0432.CCR-22-2595 GEN-009 Solid tumors Neoantigens Phase I Personalized, multi-epitope design; Immune activation observed 10.1200/JCO.2021.39.15_suppl.2613 GX-188E HPV-related cervical cancer E6/E7 epitopes of HPV16/18 Phase II Strong cellular responses and lesion regression in cervical intraepithelial neoplasia 10.1158/1078-0432.CCR-19-1513 IMA901 Renal cell carcinoma Tumor-associated peptides Phase III The magnitude of immune responses needs to be improved 10.1016/S1470-2045(16)30408-9 10.4161/21645515.2014.983857 10.1038/nm.2883 IMA950 Glioblastoma multiforme 11 glioma-associated peptides Phase I/II Safe and immunogenic 10.1093/neuonc/noz040 PolyPEPI1018 Colorectal cancer 6 HLA-restricted epitopes from 7 antigens Phase II Personalized vaccine; tested in combination with atezolizumab 10.1200/JCO.2024.42.16_suppl.3594 Open in a new tab An effective multi-subunit vaccine against cancer must include CTL and HTL epitopes in its structure. 81 , 82 CTLs are a vital component of immunity in an effective anti-tumoral response. 28 T helpers are considered the main conductors of the immune response and can act synergistically with CTLs in combating tumors. 32 In this study, we employed a two-step process to precisely choose epitopes that can effectively interact with both MHC molecules and T-cell receptors (TCRs), ensuring their immunogenic potential. According to recent studies, five different tumor-associated antigens, including ITGA2, LAMC2, BMPR2, ZFP91, and S100A16, were used for the epitope prediction. A total of 30 immunogenic epitopes (including 3 MHC I epitopes and 3 MHC II epitopes for each antigenic protein) were incorporated into the designed vaccine. B-cell epitopes, which prompt humoral immunity, were predicted based on the full-length primary sequence. Several linear and continuous B-cell epitopes were identified on the vaccine construct. Additionally, IFN-γ therapy has shown both direct and indirect effects against PC. 83 , 84 Notably, evaluations have shown that some of the vaccine’s predicted epitopes are potential IFN-γ inducers, which may further enhance the vaccine’s effectiveness. Besides choosing immunogenic epitopes, the arrangement of components in the vaccine construct is crucial. Proper linkers should be used to fuse the epitopes and adjuvants. 85 Inappropriate compartment organization may lead to structural and functional irregularities. Codon optimization of vaccine sequences is a necessary step to achieve efficient protein expression in E. coli hosts. 38 The codon adaptation index (CAI), total GC content of DNA, and mRNA secondary structure stability all indicated that the optimization process was efficiently carried out. The solubility of the protein under overexpressed conditions is critical in biochemical and functional assessments. 86 The designed protein vaccine demonstrated an acceptable level of solubility. The aliphatic index, which indicates the protein's thermal stability level, 49 showed high values, confirming that the vaccine is thermally stable. The proper molecular weight of our proposed vaccine would potentially simplify purification. The negative GRAVY score indicates that this molecule has hydrophilic properties. Protein stability is critical in the efficiency of a multi-epitope vaccine. 48 Based on the obtained results, the designed protein vaccine was stable. Besides the physicochemical attributes, a functional vaccine should ideally be non-allergenic, as allergenic responses can significantly limit its application. 87 The designed recombinant vaccine in our study was non-allergenic while maintaining strong antigenic properties. The protein secondary structure provides the essential configuration required for the three-dimensional (3D) folding of the tertiary structure. The 3D structure mainly determines protein functionality. 52 Computational analysis was utilized to assess the secondary and tertiary structures indicated that our multi-epitope vaccine is in proper condition, and the refinement process was successful. Recent studies have identified intrinsically disordered proteins as key antigenic targets for eliciting innate immune responses and promoting humoral immunity through the induction of protective antibodies. 88 , 89 In the present study, several intrinsic protein disorder regions were identified within the vaccine sequence. These regions play active roles in the improvement of the vaccine’s performance. Furthermore, Disulfide bonds are critical to the structural stability, folding, and functionality of proteins. 90 , 91 Here, based on the obtained data, two residue pairs, TYR32-ALA70 and MET347-THR350, were determined as potential disulfide bonds. MHC molecule frequencies are remarkably different among individuals from different regions of the world. This diverse collection of HLA molecules is due to the high polymorphism and polygenicity of their genes. 92 A high percentage of population coverage is necessary for a candidate vaccine. The chosen alleles for the designed vaccine demonstrated over 99% population coverage. TLRs are critical mediators of innate immunity and can notably influence subsequent acquired immune responses. 93 Potential TLR agonists can enhance the host’s immune responses and thereby improve vaccine efficacy. 94 TLR4 is a lipopolysaccharide (LPS) sensor under physiological conditions. 95 Mira A. Lanki et al. demonstrated that there is a positive association between TLR4 and a favorable prognosis of local pancreatic cancer. 96 This finding indicates that activation of TLR4 could be beneficial in controlling pancreatic cancer. In this study, the HDOCK server was used to perform the docking process between the designed vaccine and the TLR4 structure. The result of the docking process indicated a strong binding affinity between the two components. Additionally, the obtained MD simulation findings supported the presence of a stable and favorable interaction between the multi-subunit vaccine and TLR4. This interaction could potentially improve the immunogenic efficacy of the vaccine. An effective cancer vaccine should be able to elicit multiple layers of immune responses. Both innate and adaptive immunity are essential for efficiently inhibiting tumor progression. 97 , 98 Moreover, the long-term efficacy of a vaccine is highly associated with the formation of memory cells, which are generated as a result of strong immune system stimulations. 99 , 100 In this study, the C-IMMSIM server was used to simulate immune responses following a primary injection and two booster doses of the vaccine. Memory B cells and T cell populations were improved. Immunoglobulin levels, including both IgG and IgM antibodies, were strikingly elevated. In addition, our findings indicated that the IFN-γ titer was increased during this simulation period. The discussed induced immune outcomes are particularly vital for effective treatment of pancreatic cancer, which is characterized by a highly immunosuppressive tumor microenvironment. This includes dense stroma, regulatory T cells, and inhibitory cytokines that limit the effect of anti-tumor immunity measures. 101 By demonstrating enhanced memory B and T cell responses, increased IgG/IgM antibody amounts, and improved IFN-γ level, the proposed vaccine could potentially help overcome some of the mentioned immunosuppressive barriers associated with pancreatic cancer. The obtained results suggest that our proposed vaccine can evoke potent immune responses. However, these predictions are computational, and further wet lab experiments are required to confirm the actual immunogenicity of the vaccine against pancreatic cancer. Additionally, it should be noted that the utilized computational server cannot determine vaccine efficacy against pancreatic tumors, which could have been a valuable index for comprehension of the vaccine’s performance. So in vitro and in vivo studies are essential to confirm whether the vaccine can elicit similar immune responses in biological systems. Despite the promising potential of in silico-designed multi-epitope vaccines, several limitations remain. Antigen processing, a critical determinant of effective immune responses, is a complex and tightly regulated process influenced by factors such as inflammation and host genetic variability. In particular, polymorphisms in transporter associated with antigen processing (TAP) genes can significantly affect epitope presentation, potentially reducing the accuracy of computational predictions. Furthermore, the possibility of epitope cross-reactivity and the risk of immunopathological responses must be carefully considered, as these adverse effects are difficult to fully anticipate using current predictive algorithms. Given the multi-antigenic nature of peptide-based vaccines, there is an elevated risk of triggering unintended immune responses, which may not be captured through in silico analysis alone. 102 Therefore, the limitations in predicting true anti-tumor efficacy underscore the need for experimental validation. It is part of our plan to validate the immunogenicity, safety, and protective efficacy of the proposed vaccine, with comprehensive in vitro and in vivo investigations. 5. Conclusion Immunoinformatic evaluations confirmed that our multi-epitope peptide vaccine is physicochemically stable and can be effectively expressed. Furthermore, it demonstrated favorable interactions with immune receptors and stimulated strong antitumor immune responses. However, further studies, including laboratory and animal model experiments, are required to validate the vaccine’s efficacy against pancreatic cancer. Funding statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. CRediT authorship contribution statement Seyed Mostafa Rahimi: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Hamid Reza Nouri: Writing – review & editing, Supervision, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The authors would like to thank Babol University of Medical Sciences for supporting this research. Footnotes Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jgeb.2026.100670 . Appendix A. 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